The proposed experiments define how cortical neurons support robust perception of complex sounds, such as speech and other communication sounds, in natural listening environments that include background noise. Signal in noise (SIN) processing has primarily been studied psychophysically; the neural mechanisms that support the remarkable tolerance to noise exhibited by normal hearing are not well understood. We focus on the encoding of low frequency envelope information because it is crucial for intelligible speech and improving speech intelligibility for the hearing impaired is an important clinical goal. More broadly, dynamic features of sound envelopes, such as common onsets, offsets, and modulation characteristics, drive auditory scene segmentation. These features are also particularly well represented in the response dynamics of cortical neurons. However, it has proven difficult to develop a general framework for understanding cortical envelope processing because the relationship between the stimulus envelope and the neural response pattern is typically both complex and substantially nonlinear. We hypothesize that the nonlinear dynamics of cortical responses endow them with a temporal precision that is essential to the robustness of SIN processing. To test this hypothesis, we will employ a novel nonlinear modeling framework to estimate spectrotemporal receptive fields (STRFs) of neurons recorded from the core auditory fields of awake behaving squirrel monkeys using 16- channel linear probes. We will evaluate the ability of nonlinear STRF models - including reduced (e.g., linear) and modified forms - to describe the dynamics of cortical responses to sounds with simple, parametrically varied envelopes (Aim 1). We will compare the performances of the models against real neurons in encoding complex vocalizations embedded in noise (Aim 2), and test candidate neural mechanisms for 'denoising' those signals in the context of optimal Bayesian population decoding methods. Finally, we will assess the effect of attentional filtering on SIN processing by recording from animals presented with identical complex stimuli while engaged in separate tasks, only one of which requires attention to detailed envelope features (i.e., modulation frequency change detection versus sound offset detection), while simultaneously deriving STRF models for subsequent comparison (Aim 3). These experiments will provide valuable insight into candidate neural mechanisms that support both bottom-up and top-down aspects of auditory scene segmentation, and support rigorous quantitative model-based approaches to characterizing laminar transformations in the cortical representation of complex sounds.